International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 10, No. 9, 8 Sep. 2018
Cover page and Table of Contents: PDF (size: 758KB)
Optimization of Process Parameters Using Grey-Taguchi Method for Software Effort Estimation of Software Project
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Author(s)
M.Padmaja
1,*
D. Haritha
2
Index Terms
Taguchi method, Grey Relational Analysis, Orthogonal Array, Signal to Noise Ratio, ANOVA, Optimization
Abstract
Optimization is one of the techniques used in the estimation of projects to obtain the optimal parameter sequence at different levels for the best project conditions, such as size, duration and function points. In this paper, to select the significant process parameter sequence at different levels, a combination of Grey Relational Analysis (GRA) and Taguchi method applied during the estimation. This parameter sequence is essential for the industries in producing quality product at a lower cost. Taguchi method is used to improve the product quality and reduce the cost. Among the various methods of Taguchi as a standard Orthogonal Array (OA) produces better parameters to be considered at different levels. This paper uses L16 Orthogonal Array (OA) whose efficiency is proven in the experimental results. Here, a variant of GRA, GRG has been used to assign grades for projects in the dataset. Finally, the optimized process parameter sequence at different levels is obtained through the application of GRG over L16 Orthogonal Array (OA). In this paper, Grey-Taguchi method is implemented to find out the levels of software process parameters such as Duration, KSLOC, Adjustment Function Points and Raw Function Points necessary for minimizing software effort. Experimental results show that parameter levels suggested by Grey-Taguchi method result in improved GRG, which results in better software effort estimation.
Cite This Paper
M.Padmaja, D. Haritha, " Optimization of Process Parameters Using Grey-Taguchi Method for Software Effort Estimation of Software Project", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.9, pp. 10-16, 2018. DOI: 10.5815/ijigsp.2018.09.02
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